A multistep deep learning framework for the automated detection and segmentation of astrocytes in fuorescent images of brain tissue

Kayasandık, Cihan Bilge; Ru, Wenjuan; Labate, Demetrio

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fuorescent images of brain tissue

dc.contributor.author	Kayasandık, Cihan Bilge
dc.contributor.author	Ru, Wenjuan
dc.contributor.author	Labate, Demetrio
dc.date.accessioned	2020-09-28T09:20:26Z
dc.date.available	2020-09-28T09:20:26Z
dc.date.issued	2020
dc.department	İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.
dc.description.sponsorship	National Science Foundation (NSF)	en_US
dc.identifier.citation	Kayasandık, C. B., Ru, W. ve Labate, D. (2020). A multistep deep learning framework for the automated detection and segmentation of astrocytes in fuorescent images of brain tissue. Scientific Reports, 10(1). https://dx.doi.org/10.1038/s41598-020-61953-9
dc.identifier.doi	10.1038/s41598-020-61953-9
dc.identifier.issn	2045-2322
dc.identifier.issue	1
dc.identifier.scopusquality	Q1
dc.identifier.uri	https://dx.doi.org/10.1038/s41598-020-61953-9
dc.identifier.uri	https://hdl.handle.net/20.500.12511/5875
dc.identifier.volume	10
dc.identifier.wosquality	Q1
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.indekslendigikaynak	PubMed
dc.language.iso	en
dc.publisher	Nature Publishing Group
dc.relation.ispartof	Scientific Reports	en_US
dc.relation.publicationcategory	Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rights	Attribution 4.0 International	*
dc.rights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject	Brain Tissue
dc.subject	Fluorescent Images
dc.subject	Astrocytes
dc.title	A multistep deep learning framework for the automated detection and segmentation of astrocytes in fuorescent images of brain tissue
dc.type	Article